The effects of plant litter characteristics on its decomposition in soil or at the soil surface is of primary importance for adequate management of nutrients and carbon (C) in agro-ecosystems.
At present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is performed based on two or more genetically correlated traits and these are analyzed individually, selection bias may arise. Under these conditions, considering the correlation structure between the evaluated traits may provide more-accurate genetic estimates for the evaluated parameters, even under environmental influences. The present study was thus developed to examine the efficiency and applicability of multi-trait multi-environment (MTME) models by the residual maximum likelihood (REML/BLUP) and Bayesian approaches in the genetic selection of segregating soybean progeny. The study involved data pertaining to 203 soybean
F
2:4
progeny assessed in two environments for the following traits: number of days to maturity (DM), 100-seed weight (SW), and average seed yield per plot (SY). Variance components and genetic and non-genetic parameters were estimated via the REML/BLUP and Bayesian methods. The variance components estimated and the breeding values and genetic gains predicted with selection through the Bayesian procedure were similar to those obtained by REML/BLUP. The frequentist and Bayesian MTME models provided higher estimates of broad-sense heritability per plot (or heritability of total effects of progeny;
) and mean accuracy of progeny than their respective single-trait versions. Bayesian analysis provided the credibility intervals for the estimates of
. Therefore, MTME led to greater predicted gains from selection. On this basis, this procedure can be efficiently applied in the genetic selection of segregating soybean progeny.
Phytophthora root and stem rot is one of the most aggressive diseases in soybean crop. The use of resistant cultivars is the main strategy to reduce losses caused by the pathogen. This study aims to identify SNP markers associated with genes or QTLs that provide soybean with partial resistance to Phytophthora sojae. A total of 169 soybean cultivars were inoculated with Phytophthora sojae and genotyped with 3,807 SNP markers. Genome-wide association analysis was carried out via multiple linear models, followed by multiple linear regression and linkage disequilibrium analysis. Four QTLs associated with the characteristic were identified: two on chromosome 3 and two on chromosome 15. The regions containing these QTLs contain genes already annotated as providers of resistance to pathogens, in plants. The use of those markers in the selection of resistant plants can increase the efficiency of breeding programs in the development of soybean varieties resistant to P. sojae.
The selection of superior genotypes of soybean entails a simultaneous evaluation of a number of favorable traits that provide a comparatively superior yield. Disregarding the population effect in the statistical model may compromise the estimate of variance components and the prediction of genetic values. The present study was undertaken to investigate the importance of including population effect in the statistical model and to determine the effectiveness of the index based on factor analysis and ideotype design via best linear unbiased prediction (FAI-BLUP) in the selection of erect, early, and high-yielding soybean progenies. To attain these objectives, 204 soybean progenies originating from three populations were examined for various traits of agronomic interest. The inclusion of the population effect in the statistical model was relevant in the genetic evaluation of soybean progenies. To quantify the effectiveness of the FAI-BLUP index, genetic gains were predicted and compared with those obtained by the Smith-Hazel and Additive Genetic indices. The FAI-BLUP index was effective in the selection of progenies with balanced, desirable genetic gains for all traits simultaneously. Therefore, the FAI-BLUP index is an adequate tool for the simultaneous selection of important traits in soybean breeding.
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